1 Introduction

In previous notebooks and scripts, we excluded doublets and merged our Seurat object with the scRNA-seq dataset of tonsillar cells from Hamish et al.. Here, we will correct for batch effects with Harmony, and visualize the intermixing of different potential confounders pre- and post-integration.

In addition, we will save the dimensionality reduction (PCA) matrices before and after integration to further quantify the effect of the aforementioned confounders in high dimensional space.

2 Pre-processing

2.1 Load packages

library(Seurat)
library(SeuratWrappers)
library(harmony)
library(tidyverse)

2.2 Parameters

# Paths
path_to_obj <- here::here("scRNA-seq/results/R_objects/seurat_merged_with_king_et_al.rds")
path_to_save_obj <- here::here("scRNA-seq/results/R_objects/seurat_merged_with_king_et_al_integrated.rds")
path_tmp_dir <- here::here("scRNA-seq/2-QC/5-batch_effect_correction/2-data_integration_king_et_al/tmp/")
path_to_save_dimred_uncorrect <- str_c(path_tmp_dir, "batch_uncorrected_pca.rds", sep = "")
path_to_save_dimred_correct <- str_c(path_tmp_dir, "batch_corrected_pca.rds", sep = "")
path_to_save_confounders_df <- str_c(path_tmp_dir, "confounders_df.rds", sep = "") 

2.3 Load data

tonsil <- readRDS(path_to_obj)
tonsil
## An object of class Seurat 
## 29356 features across 299292 samples within 1 assay 
## Active assay: RNA (29356 features, 0 variable features)

3 Visualize UMAP without batch effect correction

# Process Seurat object
tonsil <- tonsil %>%
  NormalizeData(normalization.method = "LogNormalize", scale.factor = 1e4) %>% 
  FindVariableFeatures(nfeatures = 3000) %>%
  ScaleData() %>% 
  RunPCA() %>%
  RunUMAP(reduction = "pca", dims = 1:30)


# Visualize UMAP
confounders <- c("library_name", "sex", "age_group", "is_hashed",
                 "hospital", "assay")
umaps_before_integration <- purrr::map(confounders, function(x) {
  p <- DimPlot(tonsil, group.by = x, pt.size = 0.1)
  p
})
names(umaps_before_integration) <- confounders
print("UMAP colored by GEM:")
## [1] "UMAP colored by GEM:"
umaps_before_integration$library_name + NoLegend()

print("UMAP colored by sex, age group, cell hashing status, sampling center and assay:")
## [1] "UMAP colored by sex, age group, cell hashing status, sampling center and assay:"
umaps_before_integration[2:length(umaps_before_integration)]
## $sex

## 
## $age_group

## 
## $is_hashed

## 
## $hospital

## 
## $assay

4 Run and visualize Harmony’s integration

tonsil <- tonsil %>%
  RunHarmony(reduction = "pca", dims = 1:30, group.by.vars = "gem_id") %>%
  RunUMAP(reduction = "harmony", dims = 1:30)
umaps_after_integration <- purrr::map(confounders, function(x) {
  p <- DimPlot(tonsil, group.by = x, pt.size = 0.1)
  p
})
names(umaps_after_integration) <- confounders
print("UMAP colored by GEM:")
## [1] "UMAP colored by GEM:"
umaps_after_integration$library_name + NoLegend()

print("UMAP colored by sex, age group, cell hashing status, sampling center and assay:")
## [1] "UMAP colored by sex, age group, cell hashing status, sampling center and assay:"
umaps_after_integration[2:length(umaps_before_integration)]
## $sex

## 
## $age_group

## 
## $is_hashed

## 
## $hospital

## 
## $assay

5 Save

# If it doesn't exist create temporal directory
dir.create(path_tmp_dir, showWarnings = FALSE) 


# Save integrated Seurat object
saveRDS(tonsil, path_to_save_obj)


# Save PCA matrices to compute the Local Inverse Simpson Index (LISI)
confounders_df <- tonsil@meta.data[, confounders]
saveRDS(confounders_df, path_to_save_confounders_df)
saveRDS(
  tonsil@reductions$pca@cell.embeddings[, 1:30],
  path_to_save_dimred_uncorrect
)
saveRDS(
  tonsil@reductions$harmony@cell.embeddings[, 1:30],
  path_to_save_dimred_correct
)

6 Session Information

sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago)
## 
## Matrix products: default
## BLAS:   /apps/R/3.6.0/lib64/R/lib/libRblas.so
## LAPACK: /home/devel/rmassoni/anaconda3/lib/libmkl_rt.so
## 
## locale:
##  [1] LC_CTYPE=C                 LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.0        stringr_1.4.0        dplyr_1.0.4          purrr_0.3.4          readr_1.3.1          tidyr_1.1.0          tibble_3.0.1         ggplot2_3.3.0        tidyverse_1.3.0      harmony_1.0          Rcpp_1.0.6           SeuratWrappers_0.2.0 Seurat_3.2.0         BiocStyle_2.14.4    
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15            colorspace_1.4-1      deldir_0.1-25         ellipsis_0.3.1        ggridges_0.5.2        rprojroot_1.3-2       fs_1.4.1              rstudioapi_0.11       spatstat.data_1.4-3   farver_2.0.3          leiden_0.3.3          listenv_0.8.0         remotes_2.2.0         ggrepel_0.8.2         RSpectra_0.16-0       fansi_0.4.1           lubridate_1.7.8       xml2_1.3.2            codetools_0.2-16      splines_3.6.0         knitr_1.28            polyclip_1.10-0       jsonlite_1.7.2        broom_0.5.6           ica_1.0-2             cluster_2.1.0         dbplyr_1.4.4          png_0.1-7             uwot_0.1.8            shiny_1.4.0.2         sctransform_0.2.1     BiocManager_1.30.10   compiler_3.6.0        httr_1.4.2            backports_1.1.7       assertthat_0.2.1      Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2        cli_2.0.2             later_1.0.0           htmltools_0.4.0       tools_3.6.0           rsvd_1.0.3            igraph_1.2.5          gtable_0.3.0          glue_1.4.1            RANN_2.6.1            reshape2_1.4.4        rappdirs_0.3.1        spatstat_1.64-1       cellranger_1.1.0      vctrs_0.3.6           ape_5.3              
##  [55] nlme_3.1-148          lmtest_0.9-37         xfun_0.14             globals_0.12.5        rvest_0.3.5           mime_0.9              miniUI_0.1.1.1        lifecycle_0.2.0       irlba_2.3.3           goftest_1.2-2         future_1.17.0         MASS_7.3-51.6         zoo_1.8-8             scales_1.1.1          hms_0.5.3             promises_1.1.0        spatstat.utils_1.17-0 parallel_3.6.0        RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.16       pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15          stringi_1.4.6         rlang_0.4.10          pkgconfig_2.0.3       evaluate_0.14         lattice_0.20-41       ROCR_1.0-11           tensor_1.5            labeling_0.3          patchwork_1.0.0       htmlwidgets_1.5.1     cowplot_1.0.0         tidyselect_1.1.0      here_0.1              RcppAnnoy_0.0.16      plyr_1.8.6            magrittr_1.5          bookdown_0.19         R6_2.4.1              generics_0.0.2        DBI_1.1.0             withr_2.4.1           pillar_1.4.4          haven_2.3.1           mgcv_1.8-31           fitdistrplus_1.1-1    survival_3.1-12       abind_1.4-5           future.apply_1.5.0    modelr_0.1.8          crayon_1.3.4         
## [109] KernSmooth_2.23-17    plotly_4.9.2.1        rmarkdown_2.2         grid_3.6.0            readxl_1.3.1          data.table_1.12.8     blob_1.2.1            reprex_0.3.0          digest_0.6.20         xtable_1.8-4          httpuv_1.5.3.1        munsell_0.5.0         viridisLite_0.3.0